Ectopic pregnancy (EP) is the leading pregnancy - related cause of death in the first trimester of pregnancy and a major contributor to maternal morbidity. As the tubal pregnancy progresses, it erodes into blood vessels and can cause massive intra-abdominal bleeding. There are limitations in the strategies currently employed to diagnose EP. Even with the use of diagnostic algorithms that systematically evaluate all women at risk for an EP, only 50 percent of women with an EP can be diagnosed upon presentation to an Emergency Department (ED). Diagnosis in the remaining 50 percent represents a clinical conundrum and can take up to 6 weeks. If the diagnosis of EP is delayed, the abnormal gestation will continue to grow in the fallopian tube with potential rupture resulting in greater risks of morbidity, and mortality. Moreover, an EP of large size is not amenable to medical therapy, may require major surgery (laparotomy) instead of laparoscopy and can cause greater damage to fallopian tube (and greater impairment of fertility), even if treated before rupture. The aims of this proposal focus on this clinically relevant subpopulation of women at risk for an EP butwhose diagnosis cannot be confirmed during their initial presentation to the ED, and is thus delayed. The University of Pennsylvania Medical Center has used a systematic, validated, protocol to diagnose pregnant women who are at risk for EP since 1989. An existing electronic database chronicles the clinical course and contains the results of the diagnostic tests used to definitively diagnose women at risk for EP but not diagnosed upon presentation to the ED. We plan to use the information in this database to: 1) identify factors predictive of EP in this subgroup of pregnant women and derive a clinical prediction rule to help identify those at highest risk for EP in an attempt to shorten the time needed for diagnosis. And 2) to evaluate the serial betahcg determinations to assess the clinical utility defining deviations from the curves characteristic of a viable intrauterine pregnancy (IUP) or spontaneous miscarriage (SAB) to diagnose an EP. For these aims, we will use a retrospective cohort study design of greater than 2100 subjects. We also plan to perform a prospective cohort study, in the same study population to: 3) evaluate the utility of novel strong predictors of EP including the endometrial stripe thickness and chlamydia serology, independently, and in context with the derived prediction rule. And 4) to validate our derived prediction rule using a prospectively collected sample of women at high risk of EP. Finally, we plan for the first time, 5) to investigate if the different clinical situations in which a woman with EP are diagnosed represent differences in the natural history of EP. This proposal represents a unique opportunity to use large amounts of existing data, combined with the efficient prospective collection of data, to understand and improve upon important limitations in our ability to diagnose a reproductive disorder with important public health consequences.